The accelerating pace of digital innovation has fundamentally reshaped how educational institutions conduct their day-to-day operations. Colleges increasingly grapple with the complexity of coordinating academic workflows and institutional processes—ranging from maintaining student databases and monitoring attendance patterns to organizing class schedules, processing examination outcomes, and facilitating stakeholder communication. Conventional approaches, which depend predominantly on paper-based workflows or disjointed software tools, frequently give rise to operational bottlenecks, inconsistent datasets, and restricted information availability. This paper outlines the architectural blueprint and practical realization of a Unified College Enterprise Resource Planning (ERP) Platform that consolidates diverse academic and administrative functionalities within a single cohesive environment. The proposed platform harnesses contemporary web frameworks and adheres to a multi-layered, extensible architecture aimed at guaranteeing optimal data governance and robust system throughput. Core capabilities encompass student and instructor lifecycle management, attendance monitoring, schedule generation, institutional bulletin systems, and live bidirectional communication powered by WebSocket technology. The server-side infrastructure is engineered in Java utilizing the Spring Boot ecosystem, with Spring Security enforcing token-driven authentication via JSON Web Tokens (JWT) alongside granular, role-governed access policies. The platf+orm natively accommodates multi-tenancy, permitting distinct institutions to function autonomously on shared infrastructure while upholding strict data partitioning. PostgreSQL serves as the persistence layer, complemented by Flyway for systematic schema evolution and version tracking. Empirical evaluation of the deployed platform reveals measurable gains in operational throughput, diminished reliance on manual interventions, strengthened internal communication channels, and enriched decision-making capacity through instantaneous data retrieval. The resulting ERP platform delivers a future-proof, tamper-resistant solution tailored to the demands of contemporary academic organizations.

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Rural Self-Help Groups (SHGs) in India form the backbone of grassroots financial inclusion; however, their record-keeping remains largely paper-based and error-prone. Digitizing these ledgers introduces a critical challenge: maintaining data consistency when network connectivity is unreliable or absent. This study presents a hybrid synchronization protocol implemented in the Bharat Bachat Android application, which is specifically designed to guarantee data consistency for SHG ledger operations under intermittent network conditions. The protocol combines a local-first SQLite edge database with a conflict-aware, timestamp-driven synchronization engine that asynchronously replicates records to a centralized Laravel/MySQL backend. This study examines the consistency guarantees of this design, the mechanisms that prevent data loss or corruption during prolonged offline periods, and the conflict resolution strategies employed when multiple offline sessions converge. The results demonstrate that the proposed protocol achieves strong eventual consistency with zero data loss across disconnection periods, making it a viable and cost-effective solution for rural microfinance digitization.

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The rapid adoption of web applications and REST-based services has increased exposure to security threats, including SQL injection, cross-site scripting (XSS), cross-site request forgery (CSRF), authentication weaknesses, and improper access control mechanisms. This study examines secure web application development using ASP.NET Core together with JSON Web Token (JWT) authentication techniques. ASP.NET Core is one of the leading modern frameworks for developing efficient and secure applications, offering numerous features, including middleware request processing, model validation, dependency injection, and secure database connection via Entity Framework Core. Adding JWT authentication makes ASP.NET Core even more capable of developing safe web apps, thanks to its ability to provide stateless authentication, claims and roles-based identities and authorizations, as well as expiring tokens. Important concepts in the field of secure web development discussed here include safe coding, management of secret keys, encryption of data over HTTPS, refresh token techniques, and OWASP guidelines.

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The Payment Application Automation Testing Framework is designed to facilitate the process of end-to-end testing of financial transactions on Android based POS terminals. Nowadays, the increasing popularity of digital payment systems necessitates accurate and efficient testing of transaction workflows. The traditional approach to regression testing based on manually performed procedures is ineffective and time-consuming since it requires substantial resources to perform. This issue can be resolved through implementing a testing strategy based on automation technologies. The testing tool allows running a wide range of transactions including Card Sale, UPI, Void, Preauthorization, and Auth Completion among others. For this purpose, the system utilizes state-of-the-art software such as Appium (mobile application automation), PyTest (testing procedure execution), OpenCV (image recognition), and Allure (detailed testing report preparation). Each transaction is tested by means of conducting multiple activities that involve receipt creation, data validation, and user interface interaction. One of the main advantages of the proposed system is its ability to incorporate dynamic waiting mechanisms and activity synchronization. These features ensure higher execution speed and reliability in comparison with the traditional use of static delays. Moreover, the testing framework enables developers to conduct activities on various POS terminals with minimal code customization required. In addition, visual validation techniques increase testing accuracy since they include verification of onscreen elements and printed receipts. In conclusion, the described framework can greatly reduce testing efforts while minimizing human errors and enhancing testing cycles. Therefore, it can contribute to the improvement of software quality and increase transaction accuracy rate.

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Gram Panchayat administration in rural India remains heavily dependent on manual processes for complaint handling, notice dissemination, and welfare scheme communication — conditions that produce slow resolution cycles, poor accountability, and limited citizen awareness. This paper presents the Smart Rural Governance Portal, a web-based platform built to digitalize these core administrative functions and strengthen the connection between villagers and their local Panchayat office. Citizens can register on the platform, submit complaints supported by geo-tagged location data and photographic evidence, and monitor the progress of their submissions in real time. A dedicated administrative dashboard allows the Sarpanch to manage incoming complaints, apply keyword-driven priority classification to identify urgent issues, publish public notices, and maintain updated information on applicable government schemes. The system is developed using React for the frontend, PHP for backend logic, and MySQL for data storage, following a three-tier architecture that supports future scalability across multiple Panchayat regions. Testing confirmed improvements in complaint processing efficiency and citizen access to scheme information. The portal demonstrates that appropriately scoped digital infrastructure, built around the actual constraints of rural administration, can produce meaningful gains in governance transparency and responsiveness at the grassroots level.

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The increasing energy demand in manufacturing industries has created the need for intelligent and energy-efficient machining systems. The present study focuses on the experimental investigation and optimization of energy consumption in CNC milling using an IoT-based real-time monitoring system and Taguchi optimization method. An experimental setup consisting of current sensors, voltage sensors, power analyzer, microcontroller-based data acquisition system, and cloud-based monitoring platform was developed for continuous monitoring of machining energy consumption. The experiments were conducted on a CNC vertical milling machine under different machining conditions by varying spindle speed, feed rate, and depth of cut using an L9 orthogonal array. Real-time energy data were collected and statistically analyzed using Signal-to-Noise ratio analysis, regression analysis, and ANOVA. The experimental results revealed that machining parameters significantly influence energy consumption, with spindle speed showing the highest contribution. The optimized machining conditions resulted in considerable reduction in energy consumption and improvement in machining efficiency. The developed IoT monitoring framework successfully provided continuous real-time energy analytics and improved process visibility. The study demonstrates that the integration of IoT-based monitoring, statistical optimization, and sustainable machining strategies can effectively support intelligent and energy-efficient CNC manufacturing systems.

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Digital service platforms have transformed the way users access professional and household services. However, many current systems depend heavily on static filtering mechanisms such as category, price, or location, which often fail to deliver the most relevant service options. This limitation increases user effort, delays decision-making, and reduces overall platform efficiency. This paper presents Unified Service Hub, a web-based intelligent service matching platform designed to improve service discovery through an adaptive weighted ranking framework. Unlike conventional filtering systems, the proposed platform evaluates services using multiple dynamic parameters including provider rating, service availability, response time, provider experience, user preference, and feedback-driven trust scoring. A dynamic ranking mechanism adjusts parameter weights based on user intent, enabling personalized and context-sensitive service recommendations. The system is implemented using a React-based Single Page Application frontend, a Spring Boot RESTful backend, and a MySQL relational database. To improve responsiveness and scalability, frontend optimizations such as lazy loading, caching, and paginated service retrieval are integrated into the architecture. Experimental evaluation indicates that the proposed system improves relevance in service selection, reduces user navigation effort, and creates a more adaptive and explainable recommendation process. The proposed framework establishes a practical foundation for future AI-driven recommendation systems in digital service marketplaces.

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Cardiovascular disease (CVD) is one of the leading causes of death worldwide and, therefore, the need for accurate and interpretable computational models for clinical decision support is critical. Deep neural networks have shown great predictive capacity for many types of data; however, there are many challenges when utilizing structured clinical data for prediction due to their inability to generalise well and lack of interpretability. This research presents a framework for Hybrid Multi-Task Learning (HMTL), which combines sparse autoencoders, convolutional neural networks and an Attention Module (AM) for improved representation of features and classification of diseases. HMTL uses sparse autoencoders to produce dense latent representations of low-dimensional features, convolutional layers to learn the interactions between multiple features, and AM to prioritise clinically important information. To evaluate HMTL's generalisability, it was applied to Cleveland, Hungary and Switzerland heart disease data sets. The performance of HMTL was evaluated against traditional machine learning and other Deep Learning models. HMTL results surpassed all other models with respect to performance, achieving an average accuracy of 94.6% and AUC of 0.97. Explanations of model output demonstrate that the model's predictions align with known clinical risk factors for CVD, thereby supporting HMTL as a clinically-valid tool for decision-making.

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In high-rise steel buildings, RC shear walls play a vital role in improving seismic resistance by significantly reducing lateral displacement, story drift, and structural instability during earthquake loading. This research presents the seismic analysis of a G+10 steel building with and without RC shear wall using STAAD.Pro software. The study is to evaluate the effect of RC shear walls on displacement, base shear, and story drift of the steel structure subjected to seismic and wind loads. The type of building is regular. The study indicates that the provision of RC shear wall significantly reduces the lateral top displacement of the steel building from 36.503 mm to 11.958 mm, resulting in approximately 67.17% and improves structural stiffness.

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The growing environmental concerns associated with the cement industry, particularly its high carbon dioxide emissions and energy-intensive production processes, have accelerated the search for sustainable construction materials. Simultaneously, the large-scale generation and disposal of fly ash from thermal power plants pose significant environmental challenges. Geopolymer concrete (GPC), produced using alumino-silicate materials such as fly ash, has emerged as a promising alternative to conventional Portland cement concrete due to its reduced carbon footprint and effective utilization of industrial by-products. However, the widespread application of GPC is constrained by its dependence on heat curing, which limits its suitability for cast-in-situ construction. This study investigates the development of ambient-cured geopolymer concrete composites (GPCC) and evaluates the influence of fly ash content on their physical and mechanical properties. An extensive experimental program was conducted, including the assessment of fresh and hardened concrete characteristics and the flexural behavior of reinforced GPCC beams. The results indicated that conventional ambient-cured GPC exhibited delayed setting, requiring nearly three days for hardening. To overcome this limitation, 10% fly ash was replaced with ordinary Portland cement (OPC), resulting in GPCC with improved setting characteristics and enhanced early-age strength without the need for heat curing. The findings demonstrate the feasibility of ambient-cured GPCC as a sustainable and practical construction material for structural applications.

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The Multimodal AI-Based Student Study Preparation System is an intelligent, centralized learning platform designed to overcome the limitations of traditional digital education tools. The system integration of a multi-model AI system and fuzzy search capabilities using Fuse.js. AI-powered chatbot, fuzzy search (Fuse.js), natural language processing (NLP), voice input, optical character recognition (OCR), quiz management, and real-time performance analytics are integrated into a unified web application, enabling AI-driven personalization. This method significantly improves the efficiency and effectiveness of study practices. The system is prepared to understand user intent from diverse input, such as text, voice, or image, and return contextually relevant study materials with high precision. Experimental evaluations demonstrate that the fuzzy search module achieves approximately 94% accuracy compared to 58% for traditional exact-match systems.

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Multigate MOSFETs (multiple-gate field-effect transistors) have recently emerged as promising alternatives to conventional planar MOSFETs, which are increasingly constrained by scaling limitations. As device dimensions continue to shrink into the nanoscale regime, planar transistors experience several challenges such as pronounced short-channel effects (SCEs), increased leakage current, and reduced electrostatic control over the channel. Multigate transistor structures address these issues by employing multiple gate electrodes that surround the channel region, thereby improving gate control over charge carriers and enhancing overall device performance. This work presents a comparative assessment of various multigate MOSFET architectures, including double-gate (DG), FinFET, tri-gate, Pi-gate, omega-gate, and gate-all-around (GAA) structures. The structural characteristics, operating mechanisms, and performance benefits of each architecture are examined with respect to their suitability for nanoscale device applications. Compared with planar MOSFETs, multigate transistors offer improved electrostatic control, higher drive current capability, effective suppression of short-channel effects and significantly reduced leakage current. In addition, the role of substrate engineering in multigate transistor fabrication is discussed, particularly the use of silicon-on-insulator (SOI) technology, which contributes to improved device isolation and performance. Among these architectures, FinFET and GAA transistors are considered highly scalable and suitable for future technology nodes. Consequently, multigate transistor structures are expected to play a vital role in extending Moore's Law and enabling the development of high-performance and energy-efficient integrated circuits.

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Soil structure interaction plays a part in how well structures can handle static and dynamic loads. This study looks at how soil structure interaction and the shape of a building affect how it behaves. It considers types of foundations how stiff the soil is and the shape of the building including how tall it is, its shape and if the plan is irregular. The study uses a kind of computer simulation to see how the soil and the building interact when different loads are applied. The results show that soil structure interaction really changes how a building responds to loads especially when the soil is soft or medium. Buildings with shapes have bigger responses because the shape and the soil flexibility work together. If we do not consider soil structure interaction we might design buildings that're not safe enough especially tall and skinny ones. The study also shows that the shape of a building is very important in how it interacts with the soil and buildings with regular shapes do better than ones, with irregular shapes. The results of this study are important because they show that we need to think about soil structure interaction when we design buildings so they can handle earthquakes and other dynamic loads. This research helps engineers and designers make buildings that're safer and work better by optimizing the shape and the foundation.

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This paper presents the design, implementation, and evaluation of an integrated restaurant management and AI-assisted customer service platform, GTL Utsav Dining, that unifies customer workflows (reservations, ordering, tracking) with administrative operations (inventory, analytics, reporting) within a single full-stack architecture. The system comprises three independent subsystems: (1) a customer-facing React/Vite application supporting table bookings, online and dine-in ordering, real-time order tracking, and chatbot interaction; (2) an administrative React/Vite application providing menu management, order lifecycle control, inventory visibility, and operational reporting; and (3) a modular Python chatbot service using hybrid rule-based and machine-learning approaches for menu guidance, order tracking assistance, and FAQ handling. The backend employs Flask REST API organized into modular blueprints, MongoDB relational database with normalized schema, and independent scheduling for lead generation and event processing. Evaluation encompasses functional completeness across 45 API endpoints and 37 UI screens, automated testing of 23 chatbot scenarios achieving 100% pass rate with 94.3% intent classification accuracy, security assessment of OTP and TOTP multi-factor authentication achieving 95% and 92% success rates respectively, and operational analytics demonstrating 2.1-second order-to-dashboard synchronization time. The platform demonstrates that modern full-stack patterns can unify fragmented restaurant operations while maintaining security and conversational AI support, reducing manual operational overhead and improving decision-making latency. This implementation study contributes empirically to understanding integrated architecture patterns for hospitality domain challenges, with specific implications for small and medium-sized restaurant operations and hospitality informatics education.

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Due to the fast-paced development of technology in the digital age, there is a need to move away from the old school system to something more advanced and interconnected using the web. This paper describes the process of designing and assessing the Smart Learning Portal (SLP) which is an interconnected digital tool aimed at improving administrative efficiency and learner engagement. Unlike other available tools that face issues such as poor data integration, poor access using mobile devices, and excessive use of paper forms, the SLP employs PHP and MySQL technologies. The project was constructed based on the Agile method, which allows for incremental development and a user-oriented approach to the solution. It incorporates such important components as RBAC, automatic assessment grading, instant performance analytics, and the fully-responsive interface created with Bootstrap. The findings prove that SLP successfully fills the existing gap between conventional education and contemporary digital needs, creating an inclusive, secure, and flexible system that can be used by any academic and corporate institution. Thus, the paper proves that through automation of administrative barriers and creation of a community in the digital environment, the Smart Learning Portal ensures the solid basis for further educational developments.

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Industrial wastewater generated from dairy and distillery industries contains high concentrations of biodegradable organic matter, suspended solids, dissolved solids, and nutrients, which may cause severe environmental pollution if discharged without proper treatment. In the present study, anaerobic digestion was investigated as an effective biological treatment method for dairy wastewater, distillery wastewater, and blended wastewater under different organic loading conditions. Laboratory-scale anaerobic reactors were operated to evaluate treatment efficiency based on parameters such as Chemical Oxygen Demand (COD), Biological Oxygen Demand (BOD), pH, solids reduction, and biogas generation. The experimental results indicated significant reduction in organic pollutants along with stable methane-rich biogas production. The study demonstrates that anaerobic digestion is an economical and environmentally sustainable method for treating high-strength industrial wastewater while simultaneously recovering renewable energy. The treatment system also showed improved reactor stability under controlled loading conditions, making it suitable for industrial wastewater management applications.

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The digital era of today has made it easier and more accessible than ever before to find a quality lawyer; however, there are still many individuals who experience difficulty in finding a good lawyer due to the lack of a central authority to assist them in their search. This paper illustrates an online portal known as Legal Connect designed to allow users to find legal practitioners and maintain records. Users can search via Legal Connect for attorneys or other legal professionals based on location, expertise, and other criteria, while underlying systems provide for the safe and governed retention of records. The system is web-based using contemporary technology such as PHP and MySQL databases to allow for ease of use and greater efficiency in accessing data. The Legal Connect web-based service will not only be easier, faster, and more efficient for consumers as well as law offices than any other method of locating an attorney; in addition, it will provide an increased level of transparency throughout the process. Therefore, this case study will establish that a central authority enables the connection between attorneys and the general public, while also providing for a more effective method to maintain records.

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With the growing number of pet owners worldwide, the need for effective and intelligent pet healthcare systems has increased significantly. This research proposes a predictive pet care health system that uses data analytics techniques to monitor and evaluate the health conditions of pets. The system considers factors such as age, breed, diet, activity level, and medical history to generate health insights. The proposed approach applies data analytics methods including descriptive, diagnostic, and predictive analysis. Machine learning algorithms such as Decision Tree, Random Forest, and Logistic Regression are used to identify patterns and predict possible health risks. Data preprocessing techniques like cleaning, normalization, and feature selection are applied to improve model performance. The system is designed to assist pet owners and veterinarians by providing early warnings and recommendations. However, it is intended as a supportive tool and does not replace professional veterinary advice. The main objective of this system is to provide early warnings and useful recommendations to pet owners so that they can take timely action. While the system is helpful for basic guidance, it is not meant to replace professional veterinary advice but rather to support it.

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This paper presents a self-learning parking assistant which is created and tested in a mini-car simulation environment. The system uses a Deep Reinforcement Learning (DRL) agent to autonomously learn how to do complex left/right parallel and perpendicular parking. The DRL agent uses Proximal Policy Optimization (PPO) to produce steering and velocity commands using virtual ultrasonic range data and proprioceptive vehicle state. A reward function has been designed to penalize the DRL agent for colliding with any object, excessive maneuvering, and having a large final pose error; conversely, it will reward the DRL agent for completing the parking successfully. This study demonstrates that the proposed self-learning parking system is successful at approximately 98.5% of the time when parking in randomized environments with different types of obstructions. Additionally, the DRL agent demonstrates competitive performance results in terms of the time it takes to perform a maneuver (average) over the course of the experiments, which indicates that using (DRL) for autonomous real-time parking within limited spaces is a feasible approach. The development of new and emerging concepts such as federated learning and Proof of Stake (PoS) blockchain has also been integrated in order to provide secure sharing of data, and validate the model of the proposed self-learning parking system. Finally, the completed design, training pipeline, evaluation of experimental results, and future direction of the self-learning parking system will be discussed in this paper.

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With the rapid growth of internet usage, cybercrime has also increased, especially phishing attacks, where fake websites are created to look like real and trusted websites in order to steal users' passwords, bank details, and personal information. Traditional phishing detection methods based on fixed rules are no longer very effective because attackers keep changing their techniques. This project presents a machine-learning-based web application that can quickly check whether a website URL is safe or phishing in real time. The system analyses different features of a website, such as the URL structure, HTTPS security, domain age, and website traffic information, and then uses four machine learning algorithms — Decision Tree, Random Forest, Logistic Regression, and Support Vector Machine to classify the website. Among these, the Random Forest algorithm gave the best performance with higher accuracy and better detection results. The proposed system is lightweight, easy to use, and accessible through a web browser, making it a practical solution to improve online safety for both individuals and organisations.

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Reinforced Concrete (RC) elements such as beams and columns play a fundamental role in ensuring structural safety and stability in civil engineering structures. Accurate prediction of their load-carrying capacity is essential for safe and economical design. Traditional design approaches, based on codal provisions such as IS 456:2000 and ACI 318, rely on simplified analytical expressions derived from experimental studies. Although reliable, these methods may not fully capture the complex nonlinear interaction between material properties, geometric parameters, and reinforcement characteristics. This study presents a machine learning-based predictive framework for estimating the ultimate load-carrying capacity of reinforced concrete elements. A structured dataset comprising key structural parameters—including concrete compressive strength, steel yield strength, reinforcement ratio, section dimensions, and loading conditions—was compiled and preprocessed. Supervised learning algorithms, particularly Random Forest Regression, were implemented to model the nonlinear relationships between input parameters and structural capacity. The developed model was trained and validated using statistical performance indicators such as the coefficient of determination (R²), Mean Absolute Error (MAE), and Root Mean Square Error (RMSE). The predicted capacities were compared with traditional code-based calculations as per IS 456:2000 to evaluate consistency and reliability. The results demonstrate that the machine learning model provides accurate and efficient predictions, with strong correlation to codal values. A web-based application was developed to enable real-time capacity prediction and comparative analysis between machine learning and traditional methods. The proposed hybrid framework enhances structural design efficiency, reduces computational effort, and supports data-driven decision-making in reinforced concrete design.

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Agriculture is an important part of the economy, as it provides employment to more than 58 percent of the rural population. However, people working in this sector face several challenges, such as difficulty in finding skilled workers at the right time and limited access to essential resources like seeds and equipment. To address these issues, a web-based platform called Agro Hub has been developed. It allows farmers and workers to connect directly, making workforce management easier and more efficient. The platform also provides useful information about agricultural inputs such as seeds, fertilizers, and equipment. The system is developed using Python and Django for backend operations, while MySQL is used for data storage. The frontend is built using HTML, CSS, JavaScript, and Bootstrap to ensure a responsive design. A key feature of Agro Hub is its multilingual support, allowing users to access the platform in Marathi, Hindi, and English. This improves accessibility for users from rural areas. This paper explains the design and working of Agro Hub and highlights its role in improving agricultural practices and employment opportunities. Agro Hub acts as a digital solution that integrates technology into agriculture. It improves communication between farmers and workers and enhances overall efficiency in the agricultural sector.

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The beauty and personal care industry is undergoing a major digital shift, yet a significant number of independent salons continue to depend on manual, paper-based systems for managing their core operations. These outdated approaches create inefficiencies in appointment management, billing, customer record keeping, and overall service delivery. Style Hub is a web-based salon management system designed to replace these manual workflows with a structured, automated digital platform. Developed using HTML, CSS, JavaScript, PHP, and MySQL, the system provides separate modules for customers and administrators. Customers can register, explore available services, and schedule appointments through a clean online interface, while administrators manage the complete operational workflow including service listings, appointment confirmations, invoice generation, and business reporting from a central dashboard. This paper presents the complete design rationale, system architecture, technology stack selection, functional and non-functional requirements, and the expected outcomes of the Style Hub system. The research demonstrates how a well-structured web application can meaningfully enhance productivity, minimize human error, and elevate customer satisfaction in small to mid-sized salon businesses.

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The development industry has been scuffling with quality issues for several years, and therefore the cost to our economy is dramatic. The price could potentially be reduced significantly if the industry were to embrace the concept of quality assurance that has been used with great success by many other sectors of the economy. Building owners also have to be compelled to be educated on what's quality assurance so they'll begin using their voice to encourage adaptation of this approach to guard their investments and reduce the price of construction. Internal control (QC) and Quality Assurance (QA) represent increasingly important concerns for project managers. Defects or failures in constructed facilities may result in very large costs. Even with minor defects, re-construction is also required and facility operations impaired. Increased costs and delays are the result. Quality Assurance and internal control is an important a part of any construction process to boost the standard and uniformity of the project. The requirement for QA and QC in construction projects has increased considerably in recent times because of significant changes, advancements in technology and high expectation of the users. The QA and QC maintain uniformity in construction process and ensure more economical utilization of materials leading to significant reduction in cost to the users. The extra cost involved in QA and QC is directly proportional to the advantages. A technique has been developed for QA and QC in housing industry. The methodology accomplishes the required quality in construction process. Ultimately the presence of quality is vital. So generally we are able to define the standard in several ways as follows, Quality is conformance to requirements or specifications. Quality is fitness to be used. Quality is that the degree to which a collection of inherent characteristics fulfills requirements.

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The growing environmental concerns associated with non-degradable materials and increasing pollution have accelerated the development of advanced functional materials that are both sustainable and intelligent. This review focuses on biodegradable smart materials as an emerging class of environmentally responsive systems designed for sustainable environmental management. These materials combine the inherent degradability of biopolymers with smart functionalities such as sensitivity to external stimuli such as temperature, pH, moisture, light, and chemical environments. The paper provides a comprehensive overview of various biodegradable polymers, including natural polymers (starch, cellulose, chitosan) and synthetic biodegradable polymers such as polylactic acid (PLA), polyhydroxyalkanoates (PHA), and their nanocomposites. Special emphasis is given to the design, synthesis, and characterization of smart biodegradable systems, including hydrogels, nanocellulose-based materials, and bio-based composites, which exhibit adaptive and self-responsive behavior. The integration of nanotechnology and green chemistry approaches has significantly improved the performance, efficiency, and applicability of these materials. Despite promising advancements, challenges such as mechanical limitations, cost-effectiveness, large-scale production, and controlled degradation mechanisms remain key concerns. This review highlights recent progress, identifies research gaps, and suggests future directions for the development of high-performance biodegradable smart materials aligned with circular economy principles and sustainable development goals.

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